​Simplified system could allow for better robot-human communications

The new algorithm cuts down on unnecessary communications by robot team members, potentially improving robot-human team work in emergency response situations(Credit: Jose-Luis Olivares/MIT)

A new system, developed by researchers
at MIT, could provide a more streamlined way for robots to
communicate with humans in difficult situations, including during
emergency response operations. The new model cuts down the necessary
communications made by a robot team member by 60 percent,
significantly reducing the barrage of data that its human counterpart has to deal with.

When autonomous robots communicate,
they send each other constant updates on how the task is going,
informing one another of every tiny development. But much of this
data is superfluous to requirements, serving only to overcomplicate
the task at hand. Furthermore, every time an update is relayed by one
machine, all of its counterparts have to process the effect of the
action on their current understanding of the state of the
environment. The more information that's received, the more things can slow down.

Things are even more complex with the
most up-to-date systems, with each robot – known as an agent –
having to factor in the probability that the current accepted model
of the situation is accurate, while also considering whether future
actions will be successful. Such systems – known as decentralized
partially observable Markov decision processes, or Dec-POMDPs – are
hampered by the sheer volume of information that's constantly being
relayed between the different agents.

The MIT team set out to make things
simpler, streamlining the system to cut down the number of
communications by 60 percent. Working towards the goal of building a
system well-suited to emergency-response situations, they first
removed any prior knowledge of the agents' immediate environment from
the model.

While this was appropriate for the desired application (emergency responders often have little knowledge of the environment they're working in), it
did mean that in order to make the system work, the researchers had no choice but to also remove the
part of the system that examines the uncertainty of each actions'
effectiveness. Instead, the new system assumes that any action that's
attempted is completed successfully.

Rather than having to relay every
single piece of information, the new system allows agents to pick
from three options every time something new occurs, with the choice
of ignoring the new info, using it itself but not broadcasting it to
other agents, or both using and broadcasting the data.

Each option has pros and cons, and it's
the agents' responsibility to determine the value of each and every
decision, performing constant cost-benefit analyses based on its own
actions and the expected actions of its counterparts.

The team tested the streamlined model
using more than 300 computer-simulated rescue tasks. The results of
the robot-only study weren't actually entirely positive, with the
standard, constant-communication method actually have a better task
completion rate – between 2 and 10 percent higher than the new,
reduced communication system.

However, the researchers still have a
lot of faith in their new model, believing that the tests don't best
represent its usefulness.

"What I'd be willing to bet, although
we have to wait until we do the human-subject experiments, is that
the human-robot team will fail miserably if the system is just
telling the person all sorts of spurious information all the time,"
said paper co-author Julie Shah. "For human-robot teams, I think that this algorithm is
going to make the difference between a team that can function
effectively versus a team that just plain can't."

Looking forward, the researchers plan
to test the method with a team that includes both robot and human
agents. They've also conducted the experiments with all human agents,
using the data gathered to better understand how a human communicates
in such a situation, and using the results to improve the system
for the upcoming human-robot tests.

The findings of the project were
presented at the annual meeting of the Association for the
Advancement of Artificial Intelligence Laboratory (CSAIL) last
weekend.